{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Data model\n",
        "\n",
        "> **tldr:** This notebook introduces the NeuralGCM data model, which is built on top of `coordax`. The core data structure is `Fields` - a dictionary that maps variable names to `coordax.Field` objects (arrays with attached coordinate systems). You will learn how `Fields` are used as the standard input and output for computational primitives, how model inputs are organized into nested dictionaries to handle multiple data sources, and how `Fields` can be losslessly converted to and from `xarray.Dataset` for easy inspection, visualization, and serialization\n",
        "\n",
        "Outline:\n",
        "1. `Fields` - inputs/outputs of computation primitives\n",
        "2. Nested structure in model inputs\n",
        "3. Inspectability and lossless conversion between `coordax` and `xarray`\n",
        "\n"
      ],
      "metadata": {
        "id": "tW_MyBy5E2Cs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# imports for the tutorial.\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "\n",
        "\n",
        "from flax import nnx\n",
        "import jax\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import coordax as cx\n",
        "from neuralgcm.experimental.atmosphere import idealized_states\n",
        "from neuralgcm.experimental.core import coordinates\n",
        "from neuralgcm.experimental.core import parallelism\n",
        "from neuralgcm.experimental.core import transforms\n",
        "from neuralgcm.experimental.core import feature_transforms\n",
        "from neuralgcm.experimental.core import spherical_transforms\n",
        "from neuralgcm.experimental.core import xarray_utils"
      ],
      "metadata": {
        "id": "PNh6XimIK8gw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## `Fields` - inputs/outputs of computation primitives\n",
        "\n",
        "**Key concepts:**\n",
        "* `Fields == dict[str, cx.Field]` is a descriptive data representation\n",
        "* Most components in NeuralGCM codebase use `Fields` as inputs and outputs\n",
        "* `Fields` can be losslessly converted to and from `xarray.Dataset`"
      ],
      "metadata": {
        "id": "CmDTsAaoK_Pl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Descriptive representation using `Fields`\n",
        "\n",
        "A simulation's state (or any intermediate result) can be fully specified by its variable names, data values, and the coordinates for all array dimensions. The `Fields` structure (`dict[str, cx.Field]`) achieves this by mapping variable names to coordinate-aware arrays. This design makes the data interpretable and easy to inspect. Let's look at a demo model state represented as `Fields`"
      ],
      "metadata": {
        "id": "cY7v3HYqQLzN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sigma = coordinates.SigmaLevels.equidistant(8)\n",
        "grid = coordinates.LonLatGrid.T42()\n",
        "ylm_grid = coordinates.SphericalHarmonicGrid.T42()\n",
        "ylm_map = spherical_transforms.FixedYlmMapping(\n",
        "    grid, ylm_grid, partition_schema_key=None, mesh=parallelism.Mesh()\n",
        ")\n",
        "rng = jax.random.key(3)\n",
        "prognostics = idealized_states.perturbed_jw(\n",
        "    ylm_map, sigma, rng, as_nodal=True, temperature_format='absolute'\n",
        ")"
      ],
      "metadata": {
        "id": "JQdsY3542oiC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Default `__repr__` clearly communicates variables and associated coordinates"
      ],
      "metadata": {
        "id": "zrrD7kW93LUp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prognostics"
      ],
      "metadata": {
        "id": "KHbK2Syh23IP",
        "outputId": "9f61d17e-f474-43e9-8f3a-7cdc27d9ab9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'vorticity': <Field (\n",
              "   dims=('sigma', 'longitude', 'latitude'),\n",
              "   shape=(8, 128, 64),\n",
              "   axes={\n",
              "     'sigma': SigmaLevels(boundaries=<np float32(9,)>, ),\n",
              "     'longitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=0),\n",
              "     'latitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=1),\n",
              "   },\n",
              " ),\n",
              " 'divergence': <Field (\n",
              "   dims=('sigma', 'longitude', 'latitude'),\n",
              "   shape=(8, 128, 64),\n",
              "   axes={\n",
              "     'sigma': SigmaLevels(boundaries=<np float32(9,)>, ),\n",
              "     'longitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=0),\n",
              "     'latitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=1),\n",
              "   },\n",
              " ),\n",
              " 'log_surface_pressure': <Field (\n",
              "   dims=('longitude', 'latitude'),\n",
              "   shape=(128, 64),\n",
              "   axes={\n",
              "     'longitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=0),\n",
              "     'latitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=1),\n",
              "   },\n",
              " ),\n",
              " 'temperature': <Field (\n",
              "   dims=('sigma', 'longitude', 'latitude'),\n",
              "   shape=(8, 128, 64),\n",
              "   axes={\n",
              "     'sigma': SigmaLevels(boundaries=<np float32(9,)>, ),\n",
              "     'longitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=0),\n",
              "     'latitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=1),\n",
              "   },\n",
              " )}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fields also support visualization using `treescope`"
      ],
      "metadata": {
        "id": "bC-YhTqX4YZa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nnx.display(prognostics)"
      ],
      "metadata": {
        "id": "xRHEYmmA4MSk",
        "colab": {
          "height": 481
        },
        "outputId": "5b9634a7-ddf8-4072-dbaf-c7536b68f1c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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<treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_79b9e5f9023d49df9489b47f3167987f\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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        "### `[Fields, ...] → Fields` computation\n",
        "\n",
        "Performing computations that accept and return `Fields` ensures that inspectability and coordax safety checks apply at all stages of modeling. Most components in NeuralGCM follow this `Fields, ...` → `Fields` pattern. A large class of these modules uses the `transforms.Transform` protocol, which defines a more narrow `Fields` → `Fields` mapping. This allows such transformations to be chained together to define flexible and complex computation. They generally fall into several categories:\n",
        "* General transforms: apply element-wise `Field` transforms; change of coordinates; edit keys etc.\n",
        "* Feature transforms: inject static features; compute features based on inputs\n",
        "* Learned transforms: apply a transformation parameterized by learnable parameters\n",
        "\n",
        "An in-depth discussions of transforms and learned transforms are presented in later, not yet released \"Transforms and other modules\" and \"Neural nets, towers and learned transforms\" tutorials.\n",
        "\n",
        "Here we show a `Transform` example that uses a combination of feature transforms and general transforms to engineer a set of features that include:\n",
        "1. Latitude features\n",
        "2. All prognostics as is\n",
        "3. u_wind and v_wind components computed from modal representation\n"
      ],
      "metadata": {
        "id": "GiaOPaw-5UVO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "to_modal = transforms.ToModal(ylm_map)\n",
        "uv_from_div_curl = transforms.VelocityFromModalDivCurl(ylm_map)\n",
        "get_features_transform = transforms.Merge({\n",
        "    'lon_lat': feature_transforms.LatitudeFeatures(grid),\n",
        "    'prognostics': transforms.Identity(),\n",
        "    'u_v': transforms.Sequential([to_modal, uv_from_div_curl]),\n",
        "})"
      ],
      "metadata": {
        "id": "7-PadFQh5UM0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_features = get_features_transform(prognostics)\n",
        "for k, v in all_features.items():\n",
        "  print(f'feature \"{k}\" has dims: {v.dims}')"
      ],
      "metadata": {
        "id": "DGnurk62_SVU",
        "outputId": "2115da97-fbef-48c9-857f-13d66c9561e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "feature \"cos_latitude\" has dims: ('longitude', 'latitude')\n",
            "feature \"sin_latitude\" has dims: ('longitude', 'latitude')\n",
            "feature \"vorticity\" has dims: ('sigma', 'longitude', 'latitude')\n",
            "feature \"divergence\" has dims: ('sigma', 'longitude', 'latitude')\n",
            "feature \"log_surface_pressure\" has dims: ('longitude', 'latitude')\n",
            "feature \"temperature\" has dims: ('sigma', 'longitude', 'latitude')\n",
            "feature \"u_component_of_wind\" has dims: ('sigma', 'longitude', 'latitude')\n",
            "feature \"v_component_of_wind\" has dims: ('sigma', 'longitude', 'latitude')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Nested structure in model inputs\n",
        "\n",
        "**Key concepts:**\n",
        "* To support multiple data sources model inputs and outputs are nested in a `dict[source_name, Fields]`\n",
        "* The key of the nested structure provides identity for data source or observation setting"
      ],
      "metadata": {
        "id": "hTNSjgsi6taC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "User-facing methods for model inputs and outputs use a nested dictionary structure. This approach, which keys data by its source name (e.g., 'era5:sigma', 'ocean'), avoids name collisions (like 'ocean temperature' vs. 'air temperature') and helps partition input and output data.\n",
        "\n",
        "For example:"
      ],
      "metadata": {
        "id": "zShmmmsG7mM9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nested_data = {\n",
        "    'era5:sigma': prognostics,\n",
        "    'ocean': {'temperature': cx.wrap(293 * np.ones(grid.shape))},\n",
        "}\n",
        "print(nested_data)"
      ],
      "metadata": {
        "id": "erKdcETz6tI7",
        "outputId": "29d164b3-c2bc-4660-8a93-56613e7f6133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'era5:sigma': {'vorticity': <Field (\n",
            "  dims=('sigma', 'longitude', 'latitude'),\n",
            "  shape=(8, 128, 64),\n",
            "  axes={\n",
            "    'sigma': SigmaLevels(boundaries=<np float32(9,)>, ),\n",
            "    'longitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=0),\n",
            "    'latitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=1),\n",
            "  },\n",
            "), 'divergence': <Field (\n",
            "  dims=('sigma', 'longitude', 'latitude'),\n",
            "  shape=(8, 128, 64),\n",
            "  axes={\n",
            "    'sigma': SigmaLevels(boundaries=<np float32(9,)>, ),\n",
            "    'longitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=0),\n",
            "    'latitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=1),\n",
            "  },\n",
            "), 'log_surface_pressure': <Field (\n",
            "  dims=('longitude', 'latitude'),\n",
            "  shape=(128, 64),\n",
            "  axes={\n",
            "    'longitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=0),\n",
            "    'latitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=1),\n",
            "  },\n",
            "), 'temperature': <Field (\n",
            "  dims=('sigma', 'longitude', 'latitude'),\n",
            "  shape=(8, 128, 64),\n",
            "  axes={\n",
            "    'sigma': SigmaLevels(boundaries=<np float32(9,)>, ),\n",
            "    'longitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=0),\n",
            "    'latitude': SelectedAxis(coordinate=LonLatGrid(<5 fields...>), axis=1),\n",
            "  },\n",
            ")}, 'ocean': {'temperature': <Field dims=(None, None) shape=(128, 64) axes={} >}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we've seen above, each dictionary of `Field` object can be mapped to `xarray.Dataset`. Working with further nested structure is compatible with `xarray.DataTree`, that we eventually aspire to standardize around."
      ],
      "metadata": {
        "id": "55WGQVxv82Rf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lossless conversion between `Fields` and `xarray.Dataset`\n",
        "\n",
        "**Key concepts:**\n",
        "* `Fields` can be converted to and from `xarray.Dataset`.\n",
        "* Round trips enable easy visualization and serialization to disk\n",
        "\n",
        "Let's take a look at some examples:"
      ],
      "metadata": {
        "id": "hdQfbVRkADIh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we can inspect the prognostics `xarray.Dataset`"
      ],
      "metadata": {
        "id": "fdsRcO6bAVwX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prognostics_ds = xarray_utils.fields_to_xarray(prognostics)\n",
        "prognostics_ds"
      ],
      "metadata": {
        "id": "HZyMr9eSYe3g",
        "colab": {
          "height": 411
        },
        "outputId": "f8ba1341-1edb-4fd8-fbe3-17780d5b292d"
      },
      "execution_count": null,
      "outputs": [
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              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
              ".xr-var-data-in:checked + label > .xr-icon-database,\n",
              ".xr-index-data-in:checked + label > .xr-icon-database {\n",
              "  color: var(--xr-font-color0);\n",
              "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
              "  stroke-width: 0.8px;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 821kB\n",
              "Dimensions:               (sigma: 8, longitude: 128, latitude: 64)\n",
              "Coordinates:\n",
              "  * sigma                 (sigma) float32 32B 0.0625 0.1875 ... 0.8125 0.9375\n",
              "  * longitude             (longitude) float64 1kB 0.0 2.812 ... 354.4 357.2\n",
              "  * latitude              (latitude) float64 512B -87.86 -85.1 ... 85.1 87.86\n",
              "Data variables:\n",
              "    vorticity             (sigma, longitude, latitude) float32 262kB -2.357e-...\n",
              "    divergence            (sigma, longitude, latitude) float32 262kB 1.369e-1...\n",
              "    log_surface_pressure  (longitude, latitude) float32 33kB 11.51 ... 11.51\n",
              "    temperature           (sigma, longitude, latitude) float32 262kB 217.6 .....</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-12c7491f-3702-493d-bfd1-7140273c7ae9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-12c7491f-3702-493d-bfd1-7140273c7ae9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>sigma</span>: 8</li><li><span class='xr-has-index'>longitude</span>: 128</li><li><span class='xr-has-index'>latitude</span>: 64</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6cd462f2-cad0-4ee5-bf38-e2706aa7cd03' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6cd462f2-cad0-4ee5-bf38-e2706aa7cd03' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sigma</span></div><div class='xr-var-dims'>(sigma)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0625 0.1875 ... 0.8125 0.9375</div><input id='attrs-dec2068b-f87c-4078-90c3-bd1a607d6ff3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dec2068b-f87c-4078-90c3-bd1a607d6ff3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5066e1e-bc5d-4a71-a171-8e372d74fcb1' class='xr-var-data-in' type='checkbox'><label for='data-c5066e1e-bc5d-4a71-a171-8e372d74fcb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.0625, 0.1875, 0.3125, 0.4375, 0.5625, 0.6875, 0.8125, 0.9375],\n",
              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.812 5.625 ... 354.4 357.2</div><input id='attrs-178cad5d-b990-4d8b-a221-ce3e1dabccfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-178cad5d-b990-4d8b-a221-ce3e1dabccfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e875bd8-4837-483c-a2a4-96ae5fac2ebe' class='xr-var-data-in' type='checkbox'><label for='data-1e875bd8-4837-483c-a2a4-96ae5fac2ebe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>lon_lat_padding :</span></dt><dd>(0, 0)</dd></dl></div><div class='xr-var-data'><pre>array([  0.    ,   2.8125,   5.625 ,   8.4375,  11.25  ,  14.0625,  16.875 ,\n",
              "        19.6875,  22.5   ,  25.3125,  28.125 ,  30.9375,  33.75  ,  36.5625,\n",
              "        39.375 ,  42.1875,  45.    ,  47.8125,  50.625 ,  53.4375,  56.25  ,\n",
              "        59.0625,  61.875 ,  64.6875,  67.5   ,  70.3125,  73.125 ,  75.9375,\n",
              "        78.75  ,  81.5625,  84.375 ,  87.1875,  90.    ,  92.8125,  95.625 ,\n",
              "        98.4375, 101.25  , 104.0625, 106.875 , 109.6875, 112.5   , 115.3125,\n",
              "       118.125 , 120.9375, 123.75  , 126.5625, 129.375 , 132.1875, 135.    ,\n",
              "       137.8125, 140.625 , 143.4375, 146.25  , 149.0625, 151.875 , 154.6875,\n",
              "       157.5   , 160.3125, 163.125 , 165.9375, 168.75  , 171.5625, 174.375 ,\n",
              "       177.1875, 180.    , 182.8125, 185.625 , 188.4375, 191.25  , 194.0625,\n",
              "       196.875 , 199.6875, 202.5   , 205.3125, 208.125 , 210.9375, 213.75  ,\n",
              "       216.5625, 219.375 , 222.1875, 225.    , 227.8125, 230.625 , 233.4375,\n",
              "       236.25  , 239.0625, 241.875 , 244.6875, 247.5   , 250.3125, 253.125 ,\n",
              "       255.9375, 258.75  , 261.5625, 264.375 , 267.1875, 270.    , 272.8125,\n",
              "       275.625 , 278.4375, 281.25  , 284.0625, 286.875 , 289.6875, 292.5   ,\n",
              "       295.3125, 298.125 , 300.9375, 303.75  , 306.5625, 309.375 , 312.1875,\n",
              "       315.    , 317.8125, 320.625 , 323.4375, 326.25  , 329.0625, 331.875 ,\n",
              "       334.6875, 337.5   , 340.3125, 343.125 , 345.9375, 348.75  , 351.5625,\n",
              "       354.375 , 357.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.86 -85.1 -82.31 ... 85.1 87.86</div><input id='attrs-e92bd056-b8d8-4f53-8fc2-6a6ad5d17cc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e92bd056-b8d8-4f53-8fc2-6a6ad5d17cc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca66afd8-84be-4cce-b476-6cfc0178265e' class='xr-var-data-in' type='checkbox'><label for='data-ca66afd8-84be-4cce-b476-6cfc0178265e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>lon_lat_padding :</span></dt><dd>(0, 0)</dd></dl></div><div class='xr-var-data'><pre>array([-87.863799, -85.096527, -82.312913, -79.525607, -76.7369  , -73.947515,\n",
              "       -71.157752, -68.367756, -65.577607, -62.787352, -59.99702 , -57.206632,\n",
              "       -54.4162  , -51.625734, -48.835241, -46.044727, -43.254195, -40.463648,\n",
              "       -37.67309 , -34.882521, -32.091944, -29.30136 , -26.510769, -23.720174,\n",
              "       -20.929574, -18.138971, -15.348365, -12.557756,  -9.767146,  -6.976534,\n",
              "        -4.185921,  -1.395307,   1.395307,   4.185921,   6.976534,   9.767146,\n",
              "        12.557756,  15.348365,  18.138971,  20.929574,  23.720174,  26.510769,\n",
              "        29.30136 ,  32.091944,  34.882521,  37.67309 ,  40.463648,  43.254195,\n",
              "        46.044727,  48.835241,  51.625734,  54.4162  ,  57.206632,  59.99702 ,\n",
              "        62.787352,  65.577607,  68.367756,  71.157752,  73.947515,  76.7369  ,\n",
              "        79.525607,  82.312913,  85.096527,  87.863799])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e44a7457-6938-4e9f-b2d7-e904ac9232bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e44a7457-6938-4e9f-b2d7-e904ac9232bb' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>vorticity</span></div><div class='xr-var-dims'>(sigma, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-2.357e-06 -5.107e-06 ... 8.234e-07</div><input id='attrs-954a528d-66a0-4a23-ad55-7e4e4bb63b87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-954a528d-66a0-4a23-ad55-7e4e4bb63b87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ad3966c-576e-4504-86e6-de89d0f84683' class='xr-var-data-in' type='checkbox'><label for='data-4ad3966c-576e-4504-86e6-de89d0f84683' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-2.3567948e-06, -5.1070697e-06, -7.9808424e-06, ...,\n",
              "          7.9793308e-06,  5.1091038e-06,  2.3564014e-06],\n",
              "        [-2.3567954e-06, -5.1070247e-06, -7.9808997e-06, ...,\n",
              "          7.9792208e-06,  5.1090165e-06,  2.3564542e-06],\n",
              "        [-2.3567966e-06, -5.1069856e-06, -7.9809524e-06, ...,\n",
              "          7.9791453e-06,  5.1089360e-06,  2.3564987e-06],\n",
              "        ...,\n",
              "        [-2.3567857e-06, -5.1072552e-06, -7.9806614e-06, ...,\n",
              "          7.9799402e-06,  5.1093680e-06,  2.3561952e-06],\n",
              "        [-2.3567898e-06, -5.1071856e-06, -7.9807214e-06, ...,\n",
              "          7.9796855e-06,  5.1092843e-06,  2.3562718e-06],\n",
              "        [-2.3567927e-06, -5.1071229e-06, -7.9807814e-06, ...,\n",
              "          7.9794809e-06,  5.1091947e-06,  2.3563405e-06]],\n",
              "\n",
              "       [[-2.5018412e-06, -5.4215138e-06, -8.4722496e-06, ...,\n",
              "          8.4707426e-06,  5.4235488e-06,  2.5014533e-06],\n",
              "        [-2.5018421e-06, -5.4214697e-06, -8.4723069e-06, ...,\n",
              "          8.4706335e-06,  5.4234615e-06,  2.5015063e-06],\n",
              "        [-2.5018435e-06, -5.4214306e-06, -8.4723597e-06, ...,\n",
              "          8.4705571e-06,  5.4233824e-06,  2.5015509e-06],\n",
              "...\n",
              "          4.3376272e-06,  2.7786111e-06,  1.2810916e-06],\n",
              "        [-1.2816785e-06, -2.7764261e-06, -4.3383989e-06, ...,\n",
              "          4.3373698e-06,  2.7785272e-06,  1.2811678e-06],\n",
              "        [-1.2816814e-06, -2.7763645e-06, -4.3384607e-06, ...,\n",
              "          4.3371688e-06,  2.7784374e-06,  1.2812365e-06]],\n",
              "\n",
              "       [[-8.2380308e-07, -1.7836564e-06, -2.7872732e-06, ...,\n",
              "          2.7857659e-06,  1.7856876e-06,  8.2341364e-07],\n",
              "        [-8.2380416e-07, -1.7836104e-06, -2.7873316e-06, ...,\n",
              "          2.7856559e-06,  1.7856006e-06,  8.2346651e-07],\n",
              "        [-8.2380444e-07, -1.7835724e-06, -2.7873832e-06, ...,\n",
              "          2.7855806e-06,  1.7855200e-06,  8.2351124e-07],\n",
              "        ...,\n",
              "        [-8.2379364e-07, -1.7838418e-06, -2.7870929e-06, ...,\n",
              "          2.7863762e-06,  1.7859519e-06,  8.2320798e-07],\n",
              "        [-8.2379819e-07, -1.7837721e-06, -2.7871504e-06, ...,\n",
              "          2.7861204e-06,  1.7858679e-06,  8.2328421e-07],\n",
              "        [-8.2380109e-07, -1.7837100e-06, -2.7872120e-06, ...,\n",
              "          2.7859182e-06,  1.7857782e-06,  8.2335282e-07]]],\n",
              "      shape=(8, 128, 64), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>divergence</span></div><div class='xr-var-dims'>(sigma, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.369e-11 -6.067e-12 ... 1.526e-11</div><input id='attrs-c9f846b5-8c60-437a-a906-1a5a16651391' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9f846b5-8c60-437a-a906-1a5a16651391' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a1387f0-d38c-4cb8-94c4-b92e840ea867' class='xr-var-data-in' type='checkbox'><label for='data-7a1387f0-d38c-4cb8-94c4-b92e840ea867' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.36882910e-11, -6.06725174e-12, -3.72131215e-11, ...,\n",
              "         -1.47710469e-10,  5.62142242e-11,  1.44229411e-11],\n",
              "        [ 1.18897947e-11, -6.68292418e-12, -2.99239626e-11, ...,\n",
              "         -1.31643432e-10,  4.49939738e-11,  1.31784002e-11],\n",
              "        [ 1.00297748e-11, -6.63110799e-12, -2.34108063e-11, ...,\n",
              "         -1.12559365e-10,  3.50963043e-11,  1.15814155e-11],\n",
              "        ...,\n",
              "        [ 1.85525068e-11,  7.51272579e-13, -6.21576263e-11, ...,\n",
              "         -1.61957156e-10,  9.68773603e-11,  1.55360846e-11],\n",
              "        [ 1.70377636e-11, -2.43344819e-12, -5.36077641e-11, ...,\n",
              "         -1.64624259e-10,  8.23705687e-11,  1.56485068e-11],\n",
              "        [ 1.54100031e-11, -4.68139416e-12, -4.51787496e-11, ...,\n",
              "         -1.59274927e-10,  6.87231383e-11,  1.52626314e-11]],\n",
              "\n",
              "       [[ 1.36882910e-11, -6.06725174e-12, -3.72131215e-11, ...,\n",
              "         -1.47710469e-10,  5.62142242e-11,  1.44229411e-11],\n",
              "        [ 1.18897947e-11, -6.68292418e-12, -2.99239626e-11, ...,\n",
              "         -1.31643432e-10,  4.49939738e-11,  1.31784002e-11],\n",
              "        [ 1.00297748e-11, -6.63110799e-12, -2.34108063e-11, ...,\n",
              "         -1.12559365e-10,  3.50963043e-11,  1.15814155e-11],\n",
              "...\n",
              "         -1.61957156e-10,  9.68773603e-11,  1.55360846e-11],\n",
              "        [ 1.70377636e-11, -2.43344819e-12, -5.36077641e-11, ...,\n",
              "         -1.64624259e-10,  8.23705687e-11,  1.56485068e-11],\n",
              "        [ 1.54100031e-11, -4.68139416e-12, -4.51787496e-11, ...,\n",
              "         -1.59274927e-10,  6.87231383e-11,  1.52626314e-11]],\n",
              "\n",
              "       [[ 1.36882910e-11, -6.06725174e-12, -3.72131215e-11, ...,\n",
              "         -1.47710469e-10,  5.62142242e-11,  1.44229411e-11],\n",
              "        [ 1.18897947e-11, -6.68292418e-12, -2.99239626e-11, ...,\n",
              "         -1.31643432e-10,  4.49939738e-11,  1.31784002e-11],\n",
              "        [ 1.00297748e-11, -6.63110799e-12, -2.34108063e-11, ...,\n",
              "         -1.12559365e-10,  3.50963043e-11,  1.15814155e-11],\n",
              "        ...,\n",
              "        [ 1.85525068e-11,  7.51272579e-13, -6.21576263e-11, ...,\n",
              "         -1.61957156e-10,  9.68773603e-11,  1.55360846e-11],\n",
              "        [ 1.70377636e-11, -2.43344819e-12, -5.36077641e-11, ...,\n",
              "         -1.64624259e-10,  8.23705687e-11,  1.56485068e-11],\n",
              "        [ 1.54100031e-11, -4.68139416e-12, -4.51787496e-11, ...,\n",
              "         -1.59274927e-10,  6.87231383e-11,  1.52626314e-11]]],\n",
              "      shape=(8, 128, 64), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_surface_pressure</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>11.51 11.51 11.51 ... 11.51 11.51</div><input id='attrs-cb1e6010-fdff-4491-a367-ffcd0e8e454f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb1e6010-fdff-4491-a367-ffcd0e8e454f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e977651-05b3-4839-9cb1-d7fbb87d490c' class='xr-var-data-in' type='checkbox'><label for='data-9e977651-05b3-4839-9cb1-d7fbb87d490c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936],\n",
              "       [11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936],\n",
              "       [11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936],\n",
              "       ...,\n",
              "       [11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936],\n",
              "       [11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936],\n",
              "       [11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936]], shape=(128, 64), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(sigma, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>217.6 217.6 217.6 ... 223.9 223.8</div><input id='attrs-2780a1d1-6018-48ad-820f-8aefa09bcde5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2780a1d1-6018-48ad-820f-8aefa09bcde5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02e5be3b-793b-40d7-b1b1-f10077708387' class='xr-var-data-in' type='checkbox'><label for='data-02e5be3b-793b-40d7-b1b1-f10077708387' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ],\n",
              "        [217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ],\n",
              "        [217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ],\n",
              "        ...,\n",
              "        [217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ],\n",
              "        [217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ],\n",
              "        [217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ]],\n",
              "\n",
              "       [[227.63045, 227.62653, 227.61438, ..., 227.61438, 227.62653,\n",
              "         227.63045],\n",
              "        [227.63045, 227.62653, 227.61438, ..., 227.61438, 227.62653,\n",
              "         227.63045],\n",
              "        [227.63045, 227.62653, 227.61438, ..., 227.61438, 227.62653,\n",
              "         227.63045],\n",
              "...\n",
              "        [224.35596, 224.46065, 224.77484, ..., 224.77484, 224.46065,\n",
              "         224.35596],\n",
              "        [224.35596, 224.46065, 224.77484, ..., 224.77484, 224.46065,\n",
              "         224.35596],\n",
              "        [224.35596, 224.46065, 224.77484, ..., 224.77484, 224.46065,\n",
              "         224.35596]],\n",
              "\n",
              "       [[223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456],\n",
              "        [223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456],\n",
              "        [223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456],\n",
              "        ...,\n",
              "        [223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456],\n",
              "        [223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456],\n",
              "        [223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456]]], shape=(8, 128, 64), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2c20fc24-49c3-44da-a095-da4f8df085a7' class='xr-section-summary-in' type='checkbox'  ><label for='section-2c20fc24-49c3-44da-a095-da4f8df085a7' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>sigma</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-df3e255c-61e5-4a26-9dbc-7dd810e8199d' class='xr-index-data-in' type='checkbox'/><label for='index-df3e255c-61e5-4a26-9dbc-7dd810e8199d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0625, 0.1875, 0.3125, 0.4375, 0.5625, 0.6875, 0.8125, 0.9375], dtype=&#x27;float32&#x27;, name=&#x27;sigma&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-84421555-d003-4a3e-aaac-6253a3f88027' class='xr-index-data-in' type='checkbox'/><label for='index-84421555-d003-4a3e-aaac-6253a3f88027' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0.0,   2.8125,    5.625,   8.4375,    11.25,  14.0625,   16.875,\n",
              "        19.6875,     22.5,  25.3125,\n",
              "       ...\n",
              "        331.875, 334.6875,    337.5, 340.3125,  343.125, 345.9375,   348.75,\n",
              "       351.5625,  354.375, 357.1875],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=128))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e713b4e4-b09f-4f4a-ad77-22837a294cd3' class='xr-index-data-in' type='checkbox'/><label for='index-e713b4e4-b09f-4f4a-ad77-22837a294cd3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -87.86379883923263,   -85.0965269883173,  -82.31291294788628,\n",
              "        -79.52560657265944,  -76.73689968036831,  -73.94751515398967,\n",
              "        -71.15775201158732,  -68.36775610831317,  -65.57760701082782,\n",
              "       -62.787351798963066,   -59.9970201084913, -57.206631527643246,\n",
              "        -54.41619952608621,  -51.62573367493825,  -48.83524096625059,\n",
              "       -46.044726631101675,  -43.25419466535093,  -40.46364817811504,\n",
              "        -37.67308962904533, -34.882520993773454,  -32.09194388174401,\n",
              "       -29.301359621762735, -26.510769325210994, -23.720173933534753,\n",
              "        -20.92957425448951, -18.138970990239358, -15.348364759491494,\n",
              "       -12.557756115230678,  -9.767145559195567,  -6.976533553948635,\n",
              "       -4.1859205331891545, -1.3953069108194969,  1.3953069108194969,\n",
              "        4.1859205331891545,   6.976533553948635,   9.767145559195567,\n",
              "        12.557756115230678,  15.348364759491494,  18.138970990239358,\n",
              "         20.92957425448951,  23.720173933534753,  26.510769325210994,\n",
              "        29.301359621762735,   32.09194388174401,  34.882520993773454,\n",
              "         37.67308962904533,   40.46364817811504,   43.25419466535093,\n",
              "        46.044726631101675,   48.83524096625059,   51.62573367493825,\n",
              "         54.41619952608621,  57.206631527643246,    59.9970201084913,\n",
              "        62.787351798963066,   65.57760701082782,   68.36775610831317,\n",
              "         71.15775201158732,   73.94751515398967,   76.73689968036831,\n",
              "         79.52560657265944,   82.31291294788628,    85.0965269883173,\n",
              "         87.86379883923263],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b838f41-6751-4b1b-ad22-bfd305e65554' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5b838f41-6751-4b1b-ad22-bfd305e65554' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
            ],
            "text/plain": [
              "<xarray.Dataset> Size: 821kB\n",
              "Dimensions:               (sigma: 8, longitude: 128, latitude: 64)\n",
              "Coordinates:\n",
              "  * sigma                 (sigma) float32 32B 0.0625 0.1875 ... 0.8125 0.9375\n",
              "  * longitude             (longitude) float64 1kB 0.0 2.812 ... 354.4 357.2\n",
              "  * latitude              (latitude) float64 512B -87.86 -85.1 ... 85.1 87.86\n",
              "Data variables:\n",
              "    vorticity             (sigma, longitude, latitude) float32 262kB -2.357e-...\n",
              "    divergence            (sigma, longitude, latitude) float32 262kB 1.369e-1...\n",
              "    log_surface_pressure  (longitude, latitude) float32 33kB 11.51 ... 11.51\n",
              "    temperature           (sigma, longitude, latitude) float32 262kB 217.6 ....."
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing intermediate results is an effective way to check computations"
      ],
      "metadata": {
        "id": "-o-GuZpIApT0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prognostics_ds.divergence.isel(sigma=-1).plot(x='longitude', y='latitude')"
      ],
      "metadata": {
        "id": "GZw4wwIt4617",
        "colab": {
          "height": 311
        },
        "outputId": "17afd4be-3a65-43b3-c64b-713bfccd7bb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x5244c7440830>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 277,
              "width": 396
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also convert back to the `coordax` representation, potentially after saving it to disk."
      ],
      "metadata": {
        "id": "_pp6NgvP5EYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "restored_state = xarray_utils.xarray_to_fields(prognostics_ds)\n",
        "(restored_state['divergence'] == prognostics['divergence']).data.all()"
      ],
      "metadata": {
        "id": "NWS4OvQ2ZZ_j",
        "outputId": "9d23d1b9-574d-4f61-ce5a-28b3a97c453f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Array(True, dtype=bool)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since most computations deal with `Fields`, we can also easily inspect the features we engineered:"
      ],
      "metadata": {
        "id": "VZmNhAc_ZSWl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features_ds = xarray_utils.fields_to_xarray(all_features)\n",
        "features_ds"
      ],
      "metadata": {
        "id": "L4-5kiNIBIdu",
        "colab": {
          "height": 511
        },
        "outputId": "6a134e7c-c7e9-4b15-eefe-7a78001caabc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              ".xr-section-item input:enabled + label {\n",
              "  cursor: pointer;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-item input:focus + label {\n",
              "  border: 2px solid var(--xr-font-color0) !important;\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label:hover {\n",
              "  color: var(--xr-font-color0);\n",
              "}\n",
              "\n",
              ".xr-section-summary {\n",
              "  grid-column: 1;\n",
              "  color: var(--xr-font-color2);\n",
              "  font-weight: 500;\n",
              "}\n",
              "\n",
              ".xr-section-summary > span {\n",
              "  display: inline-block;\n",
              "  padding-left: 0.5em;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in + label:before {\n",
              "  display: inline-block;\n",
              "  content: \"►\";\n",
              "  font-size: 11px;\n",
              "  width: 15px;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label:before {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label:before {\n",
              "  content: \"▼\";\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label > span {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-summary,\n",
              ".xr-section-inline-details {\n",
              "  padding-top: 4px;\n",
              "  padding-bottom: 4px;\n",
              "}\n",
              "\n",
              ".xr-section-inline-details {\n",
              "  grid-column: 2 / -1;\n",
              "}\n",
              "\n",
              ".xr-section-details {\n",
              "  display: none;\n",
              "  grid-column: 1 / -1;\n",
              "  margin-bottom: 5px;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-array-wrap {\n",
              "  grid-column: 1 / -1;\n",
              "  display: grid;\n",
              "  grid-template-columns: 20px auto;\n",
              "}\n",
              "\n",
              ".xr-array-wrap > label {\n",
              "  grid-column: 1;\n",
              "  vertical-align: top;\n",
              "}\n",
              "\n",
              ".xr-preview {\n",
              "  color: var(--xr-font-color3);\n",
              "}\n",
              "\n",
              ".xr-array-preview,\n",
              ".xr-array-data {\n",
              "  padding: 0 5px !important;\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-array-data,\n",
              ".xr-array-in:checked ~ .xr-array-preview {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-array-in:checked ~ .xr-array-data,\n",
              ".xr-array-preview {\n",
              "  display: inline-block;\n",
              "}\n",
              "\n",
              ".xr-dim-list {\n",
              "  display: inline-block !important;\n",
              "  list-style: none;\n",
              "  padding: 0 !important;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list li {\n",
              "  display: inline-block;\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list:before {\n",
              "  content: \"(\";\n",
              "}\n",
              "\n",
              ".xr-dim-list:after {\n",
              "  content: \")\";\n",
              "}\n",
              "\n",
              ".xr-dim-list li:not(:last-child):after {\n",
              "  content: \",\";\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-has-index {\n",
              "  font-weight: bold;\n",
              "}\n",
              "\n",
              ".xr-var-list,\n",
              ".xr-var-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-var-item > div,\n",
              ".xr-var-item label,\n",
              ".xr-var-item > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-even);\n",
              "  border-color: var(--xr-background-color-row-odd);\n",
              "  margin-bottom: 0;\n",
              "  padding-top: 2px;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "  border-color: var(--xr-background-color-row-even);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  border-top: 2px dotted var(--xr-background-color);\n",
              "  padding-bottom: 20px !important;\n",
              "  padding-top: 10px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in + label,\n",
              ".xr-var-data-in + label,\n",
              ".xr-index-data-in + label {\n",
              "  padding: 0 1px;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-data > pre,\n",
              ".xr-index-data > pre,\n",
              ".xr-var-data > table > tbody > tr {\n",
              "  background-color: transparent !important;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
              ".xr-var-data-in:checked + label > .xr-icon-database,\n",
              ".xr-index-data-in:checked + label > .xr-icon-database {\n",
              "  color: var(--xr-font-color0);\n",
              "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
              "  stroke-width: 0.8px;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1MB\n",
              "Dimensions:               (longitude: 128, latitude: 64, sigma: 8)\n",
              "Coordinates:\n",
              "  * longitude             (longitude) float64 1kB 0.0 2.812 ... 354.4 357.2\n",
              "  * latitude              (latitude) float64 512B -87.86 -85.1 ... 85.1 87.86\n",
              "  * sigma                 (sigma) float32 32B 0.0625 0.1875 ... 0.8125 0.9375\n",
              "Data variables:\n",
              "    cos_latitude          (longitude, latitude) float32 33kB 0.03728 ... 0.03728\n",
              "    sin_latitude          (longitude, latitude) float32 33kB -0.9993 ... 0.9993\n",
              "    vorticity             (sigma, longitude, latitude) float32 262kB -2.357e-...\n",
              "    divergence            (sigma, longitude, latitude) float32 262kB 1.369e-1...\n",
              "    log_surface_pressure  (longitude, latitude) float32 33kB 11.51 ... 11.51\n",
              "    temperature           (sigma, longitude, latitude) float32 262kB 217.6 .....\n",
              "    u_component_of_wind   (sigma, longitude, latitude) float32 262kB 2.952e-0...\n",
              "    v_component_of_wind   (sigma, longitude, latitude) float32 262kB -1.75e-1...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-013b20a5-6903-4cb0-af93-dc4600ff817e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-013b20a5-6903-4cb0-af93-dc4600ff817e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>longitude</span>: 128</li><li><span class='xr-has-index'>latitude</span>: 64</li><li><span class='xr-has-index'>sigma</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-82dfa6f0-5d61-4f25-be6f-7afc5624c8ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-82dfa6f0-5d61-4f25-be6f-7afc5624c8ae' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.812 5.625 ... 354.4 357.2</div><input id='attrs-e278f73f-f537-4b44-b810-84017ff1e120' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e278f73f-f537-4b44-b810-84017ff1e120' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5adc912-f413-41e8-9202-718b5ac7d762' class='xr-var-data-in' type='checkbox'><label for='data-b5adc912-f413-41e8-9202-718b5ac7d762' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>lon_lat_padding :</span></dt><dd>(0, 0)</dd></dl></div><div class='xr-var-data'><pre>array([  0.    ,   2.8125,   5.625 ,   8.4375,  11.25  ,  14.0625,  16.875 ,\n",
              "        19.6875,  22.5   ,  25.3125,  28.125 ,  30.9375,  33.75  ,  36.5625,\n",
              "        39.375 ,  42.1875,  45.    ,  47.8125,  50.625 ,  53.4375,  56.25  ,\n",
              "        59.0625,  61.875 ,  64.6875,  67.5   ,  70.3125,  73.125 ,  75.9375,\n",
              "        78.75  ,  81.5625,  84.375 ,  87.1875,  90.    ,  92.8125,  95.625 ,\n",
              "        98.4375, 101.25  , 104.0625, 106.875 , 109.6875, 112.5   , 115.3125,\n",
              "       118.125 , 120.9375, 123.75  , 126.5625, 129.375 , 132.1875, 135.    ,\n",
              "       137.8125, 140.625 , 143.4375, 146.25  , 149.0625, 151.875 , 154.6875,\n",
              "       157.5   , 160.3125, 163.125 , 165.9375, 168.75  , 171.5625, 174.375 ,\n",
              "       177.1875, 180.    , 182.8125, 185.625 , 188.4375, 191.25  , 194.0625,\n",
              "       196.875 , 199.6875, 202.5   , 205.3125, 208.125 , 210.9375, 213.75  ,\n",
              "       216.5625, 219.375 , 222.1875, 225.    , 227.8125, 230.625 , 233.4375,\n",
              "       236.25  , 239.0625, 241.875 , 244.6875, 247.5   , 250.3125, 253.125 ,\n",
              "       255.9375, 258.75  , 261.5625, 264.375 , 267.1875, 270.    , 272.8125,\n",
              "       275.625 , 278.4375, 281.25  , 284.0625, 286.875 , 289.6875, 292.5   ,\n",
              "       295.3125, 298.125 , 300.9375, 303.75  , 306.5625, 309.375 , 312.1875,\n",
              "       315.    , 317.8125, 320.625 , 323.4375, 326.25  , 329.0625, 331.875 ,\n",
              "       334.6875, 337.5   , 340.3125, 343.125 , 345.9375, 348.75  , 351.5625,\n",
              "       354.375 , 357.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.86 -85.1 -82.31 ... 85.1 87.86</div><input id='attrs-97cc7621-dff6-4c40-b685-f6e1b7ec4a5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97cc7621-dff6-4c40-b685-f6e1b7ec4a5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffeef1cc-be91-4c8b-8843-2731fc88f26f' class='xr-var-data-in' type='checkbox'><label for='data-ffeef1cc-be91-4c8b-8843-2731fc88f26f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>lon_lat_padding :</span></dt><dd>(0, 0)</dd></dl></div><div class='xr-var-data'><pre>array([-87.863799, -85.096527, -82.312913, -79.525607, -76.7369  , -73.947515,\n",
              "       -71.157752, -68.367756, -65.577607, -62.787352, -59.99702 , -57.206632,\n",
              "       -54.4162  , -51.625734, -48.835241, -46.044727, -43.254195, -40.463648,\n",
              "       -37.67309 , -34.882521, -32.091944, -29.30136 , -26.510769, -23.720174,\n",
              "       -20.929574, -18.138971, -15.348365, -12.557756,  -9.767146,  -6.976534,\n",
              "        -4.185921,  -1.395307,   1.395307,   4.185921,   6.976534,   9.767146,\n",
              "        12.557756,  15.348365,  18.138971,  20.929574,  23.720174,  26.510769,\n",
              "        29.30136 ,  32.091944,  34.882521,  37.67309 ,  40.463648,  43.254195,\n",
              "        46.044727,  48.835241,  51.625734,  54.4162  ,  57.206632,  59.99702 ,\n",
              "        62.787352,  65.577607,  68.367756,  71.157752,  73.947515,  76.7369  ,\n",
              "        79.525607,  82.312913,  85.096527,  87.863799])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sigma</span></div><div class='xr-var-dims'>(sigma)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0625 0.1875 ... 0.8125 0.9375</div><input id='attrs-116f52f1-8e52-4aa7-a71d-bfce3b6c3e0e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-116f52f1-8e52-4aa7-a71d-bfce3b6c3e0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0eaf9dc-8688-4ae0-856f-4070277a394d' class='xr-var-data-in' type='checkbox'><label for='data-d0eaf9dc-8688-4ae0-856f-4070277a394d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.0625, 0.1875, 0.3125, 0.4375, 0.5625, 0.6875, 0.8125, 0.9375],\n",
              "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1b29c8a7-23ec-4296-8c31-3a0409c7cd49' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1b29c8a7-23ec-4296-8c31-3a0409c7cd49' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cos_latitude</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.03728 0.08548 ... 0.08548 0.03728</div><input id='attrs-6ecb31f6-bfdf-462d-a53e-95d41eb2c419' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ecb31f6-bfdf-462d-a53e-95d41eb2c419' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef2eb729-0cb6-4c90-ae7a-aced2a902f2f' class='xr-var-data-in' type='checkbox'><label for='data-ef2eb729-0cb6-4c90-ae7a-aced2a902f2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.0372751 , 0.08547731, 0.13376287, ..., 0.13376287, 0.08547731,\n",
              "        0.0372751 ],\n",
              "       [0.0372751 , 0.08547731, 0.13376287, ..., 0.13376287, 0.08547731,\n",
              "        0.0372751 ],\n",
              "       [0.0372751 , 0.08547731, 0.13376287, ..., 0.13376287, 0.08547731,\n",
              "        0.0372751 ],\n",
              "       ...,\n",
              "       [0.0372751 , 0.08547731, 0.13376287, ..., 0.13376287, 0.08547731,\n",
              "        0.0372751 ],\n",
              "       [0.0372751 , 0.08547731, 0.13376287, ..., 0.13376287, 0.08547731,\n",
              "        0.0372751 ],\n",
              "       [0.0372751 , 0.08547731, 0.13376287, ..., 0.13376287, 0.08547731,\n",
              "        0.0372751 ]], shape=(128, 64), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sin_latitude</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.9993 -0.9963 ... 0.9963 0.9993</div><input id='attrs-7d03ef0e-8665-4472-8de0-b8248f544c82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d03ef0e-8665-4472-8de0-b8248f544c82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9792228e-1ea6-4df1-b3e3-63e50591179a' class='xr-var-data-in' type='checkbox'><label for='data-9792228e-1ea6-4df1-b3e3-63e50591179a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.99930507, -0.9963401 , -0.99101335, ...,  0.99101335,\n",
              "         0.9963401 ,  0.99930507],\n",
              "       [-0.99930507, -0.9963401 , -0.99101335, ...,  0.99101335,\n",
              "         0.9963401 ,  0.99930507],\n",
              "       [-0.99930507, -0.9963401 , -0.99101335, ...,  0.99101335,\n",
              "         0.9963401 ,  0.99930507],\n",
              "       ...,\n",
              "       [-0.99930507, -0.9963401 , -0.99101335, ...,  0.99101335,\n",
              "         0.9963401 ,  0.99930507],\n",
              "       [-0.99930507, -0.9963401 , -0.99101335, ...,  0.99101335,\n",
              "         0.9963401 ,  0.99930507],\n",
              "       [-0.99930507, -0.9963401 , -0.99101335, ...,  0.99101335,\n",
              "         0.9963401 ,  0.99930507]], shape=(128, 64), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vorticity</span></div><div class='xr-var-dims'>(sigma, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-2.357e-06 -5.107e-06 ... 8.234e-07</div><input id='attrs-e21ed35a-6056-4363-9042-064b4b6e5131' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e21ed35a-6056-4363-9042-064b4b6e5131' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59bda04c-4150-4d48-a69a-1b73f0e0007a' class='xr-var-data-in' type='checkbox'><label for='data-59bda04c-4150-4d48-a69a-1b73f0e0007a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-2.3567948e-06, -5.1070697e-06, -7.9808424e-06, ...,\n",
              "          7.9793308e-06,  5.1091038e-06,  2.3564014e-06],\n",
              "        [-2.3567954e-06, -5.1070247e-06, -7.9808997e-06, ...,\n",
              "          7.9792208e-06,  5.1090165e-06,  2.3564542e-06],\n",
              "        [-2.3567966e-06, -5.1069856e-06, -7.9809524e-06, ...,\n",
              "          7.9791453e-06,  5.1089360e-06,  2.3564987e-06],\n",
              "        ...,\n",
              "        [-2.3567857e-06, -5.1072552e-06, -7.9806614e-06, ...,\n",
              "          7.9799402e-06,  5.1093680e-06,  2.3561952e-06],\n",
              "        [-2.3567898e-06, -5.1071856e-06, -7.9807214e-06, ...,\n",
              "          7.9796855e-06,  5.1092843e-06,  2.3562718e-06],\n",
              "        [-2.3567927e-06, -5.1071229e-06, -7.9807814e-06, ...,\n",
              "          7.9794809e-06,  5.1091947e-06,  2.3563405e-06]],\n",
              "\n",
              "       [[-2.5018412e-06, -5.4215138e-06, -8.4722496e-06, ...,\n",
              "          8.4707426e-06,  5.4235488e-06,  2.5014533e-06],\n",
              "        [-2.5018421e-06, -5.4214697e-06, -8.4723069e-06, ...,\n",
              "          8.4706335e-06,  5.4234615e-06,  2.5015063e-06],\n",
              "        [-2.5018435e-06, -5.4214306e-06, -8.4723597e-06, ...,\n",
              "          8.4705571e-06,  5.4233824e-06,  2.5015509e-06],\n",
              "...\n",
              "          4.3376272e-06,  2.7786111e-06,  1.2810916e-06],\n",
              "        [-1.2816785e-06, -2.7764261e-06, -4.3383989e-06, ...,\n",
              "          4.3373698e-06,  2.7785272e-06,  1.2811678e-06],\n",
              "        [-1.2816814e-06, -2.7763645e-06, -4.3384607e-06, ...,\n",
              "          4.3371688e-06,  2.7784374e-06,  1.2812365e-06]],\n",
              "\n",
              "       [[-8.2380308e-07, -1.7836564e-06, -2.7872732e-06, ...,\n",
              "          2.7857659e-06,  1.7856876e-06,  8.2341364e-07],\n",
              "        [-8.2380416e-07, -1.7836104e-06, -2.7873316e-06, ...,\n",
              "          2.7856559e-06,  1.7856006e-06,  8.2346651e-07],\n",
              "        [-8.2380444e-07, -1.7835724e-06, -2.7873832e-06, ...,\n",
              "          2.7855806e-06,  1.7855200e-06,  8.2351124e-07],\n",
              "        ...,\n",
              "        [-8.2379364e-07, -1.7838418e-06, -2.7870929e-06, ...,\n",
              "          2.7863762e-06,  1.7859519e-06,  8.2320798e-07],\n",
              "        [-8.2379819e-07, -1.7837721e-06, -2.7871504e-06, ...,\n",
              "          2.7861204e-06,  1.7858679e-06,  8.2328421e-07],\n",
              "        [-8.2380109e-07, -1.7837100e-06, -2.7872120e-06, ...,\n",
              "          2.7859182e-06,  1.7857782e-06,  8.2335282e-07]]],\n",
              "      shape=(8, 128, 64), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>divergence</span></div><div class='xr-var-dims'>(sigma, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.369e-11 -6.067e-12 ... 1.526e-11</div><input id='attrs-33469cd0-510e-4174-a977-3380f8337b4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33469cd0-510e-4174-a977-3380f8337b4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1c9b923-08c6-46aa-97f6-6bdbb33fbe46' class='xr-var-data-in' type='checkbox'><label for='data-f1c9b923-08c6-46aa-97f6-6bdbb33fbe46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.36882910e-11, -6.06725174e-12, -3.72131215e-11, ...,\n",
              "         -1.47710469e-10,  5.62142242e-11,  1.44229411e-11],\n",
              "        [ 1.18897947e-11, -6.68292418e-12, -2.99239626e-11, ...,\n",
              "         -1.31643432e-10,  4.49939738e-11,  1.31784002e-11],\n",
              "        [ 1.00297748e-11, -6.63110799e-12, -2.34108063e-11, ...,\n",
              "         -1.12559365e-10,  3.50963043e-11,  1.15814155e-11],\n",
              "        ...,\n",
              "        [ 1.85525068e-11,  7.51272579e-13, -6.21576263e-11, ...,\n",
              "         -1.61957156e-10,  9.68773603e-11,  1.55360846e-11],\n",
              "        [ 1.70377636e-11, -2.43344819e-12, -5.36077641e-11, ...,\n",
              "         -1.64624259e-10,  8.23705687e-11,  1.56485068e-11],\n",
              "        [ 1.54100031e-11, -4.68139416e-12, -4.51787496e-11, ...,\n",
              "         -1.59274927e-10,  6.87231383e-11,  1.52626314e-11]],\n",
              "\n",
              "       [[ 1.36882910e-11, -6.06725174e-12, -3.72131215e-11, ...,\n",
              "         -1.47710469e-10,  5.62142242e-11,  1.44229411e-11],\n",
              "        [ 1.18897947e-11, -6.68292418e-12, -2.99239626e-11, ...,\n",
              "         -1.31643432e-10,  4.49939738e-11,  1.31784002e-11],\n",
              "        [ 1.00297748e-11, -6.63110799e-12, -2.34108063e-11, ...,\n",
              "         -1.12559365e-10,  3.50963043e-11,  1.15814155e-11],\n",
              "...\n",
              "         -1.61957156e-10,  9.68773603e-11,  1.55360846e-11],\n",
              "        [ 1.70377636e-11, -2.43344819e-12, -5.36077641e-11, ...,\n",
              "         -1.64624259e-10,  8.23705687e-11,  1.56485068e-11],\n",
              "        [ 1.54100031e-11, -4.68139416e-12, -4.51787496e-11, ...,\n",
              "         -1.59274927e-10,  6.87231383e-11,  1.52626314e-11]],\n",
              "\n",
              "       [[ 1.36882910e-11, -6.06725174e-12, -3.72131215e-11, ...,\n",
              "         -1.47710469e-10,  5.62142242e-11,  1.44229411e-11],\n",
              "        [ 1.18897947e-11, -6.68292418e-12, -2.99239626e-11, ...,\n",
              "         -1.31643432e-10,  4.49939738e-11,  1.31784002e-11],\n",
              "        [ 1.00297748e-11, -6.63110799e-12, -2.34108063e-11, ...,\n",
              "         -1.12559365e-10,  3.50963043e-11,  1.15814155e-11],\n",
              "        ...,\n",
              "        [ 1.85525068e-11,  7.51272579e-13, -6.21576263e-11, ...,\n",
              "         -1.61957156e-10,  9.68773603e-11,  1.55360846e-11],\n",
              "        [ 1.70377636e-11, -2.43344819e-12, -5.36077641e-11, ...,\n",
              "         -1.64624259e-10,  8.23705687e-11,  1.56485068e-11],\n",
              "        [ 1.54100031e-11, -4.68139416e-12, -4.51787496e-11, ...,\n",
              "         -1.59274927e-10,  6.87231383e-11,  1.52626314e-11]]],\n",
              "      shape=(8, 128, 64), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_surface_pressure</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>11.51 11.51 11.51 ... 11.51 11.51</div><input id='attrs-3a5cce79-d850-4fa6-a644-401efc03d168' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a5cce79-d850-4fa6-a644-401efc03d168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6112d87-5c3d-4706-a2d7-7a9061738711' class='xr-var-data-in' type='checkbox'><label for='data-e6112d87-5c3d-4706-a2d7-7a9061738711' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936],\n",
              "       [11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936],\n",
              "       [11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936],\n",
              "       ...,\n",
              "       [11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936],\n",
              "       [11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936],\n",
              "       [11.512936, 11.512927, 11.512929, ..., 11.512929, 11.512927,\n",
              "        11.512936]], shape=(128, 64), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(sigma, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>217.6 217.6 217.6 ... 223.9 223.8</div><input id='attrs-5b9e80f6-6117-493f-8528-d60b961c8f2a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b9e80f6-6117-493f-8528-d60b961c8f2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef2982d8-4cd9-466a-ae30-47227ec32e20' class='xr-var-data-in' type='checkbox'><label for='data-ef2982d8-4cd9-466a-ae30-47227ec32e20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ],\n",
              "        [217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ],\n",
              "        [217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ],\n",
              "        ...,\n",
              "        [217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ],\n",
              "        [217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ],\n",
              "        [217.6302 , 217.62645, 217.61505, ..., 217.61505, 217.62645,\n",
              "         217.6302 ]],\n",
              "\n",
              "       [[227.63045, 227.62653, 227.61438, ..., 227.61438, 227.62653,\n",
              "         227.63045],\n",
              "        [227.63045, 227.62653, 227.61438, ..., 227.61438, 227.62653,\n",
              "         227.63045],\n",
              "        [227.63045, 227.62653, 227.61438, ..., 227.61438, 227.62653,\n",
              "         227.63045],\n",
              "...\n",
              "        [224.35596, 224.46065, 224.77484, ..., 224.77484, 224.46065,\n",
              "         224.35596],\n",
              "        [224.35596, 224.46065, 224.77484, ..., 224.77484, 224.46065,\n",
              "         224.35596],\n",
              "        [224.35596, 224.46065, 224.77484, ..., 224.77484, 224.46065,\n",
              "         224.35596]],\n",
              "\n",
              "       [[223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456],\n",
              "        [223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456],\n",
              "        [223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456],\n",
              "        ...,\n",
              "        [223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456],\n",
              "        [223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456],\n",
              "        [223.78456, 223.90332, 224.25829, ..., 224.25829, 223.90332,\n",
              "         223.78456]]], shape=(8, 128, 64), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_component_of_wind</span></div><div class='xr-var-dims'>(sigma, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.952e-08 1.515e-07 ... 9.448e-09</div><input id='attrs-1878d809-72eb-463d-9476-ca3164faacb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1878d809-72eb-463d-9476-ca3164faacb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9d663ca-100e-4496-aa44-d0b582f8b50f' class='xr-var-data-in' type='checkbox'><label for='data-e9d663ca-100e-4496-aa44-d0b582f8b50f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[2.95178957e-08, 1.51467390e-07, 3.60887839e-07, ...,\n",
              "         3.60833440e-07, 1.52221887e-07, 2.84405637e-08],\n",
              "        [2.95086267e-08, 1.51462800e-07, 3.60900088e-07, ...,\n",
              "         3.60794331e-07, 1.52230626e-07, 2.84653829e-08],\n",
              "        [2.95005460e-08, 1.51459389e-07, 3.60910434e-07, ...,\n",
              "         3.60761305e-07, 1.52236424e-07, 2.84869763e-08],\n",
              "        ...,\n",
              "        [2.95515239e-08, 1.51490923e-07, 3.60841256e-07, ...,\n",
              "         3.60981858e-07, 1.52169591e-07, 2.83499606e-08],\n",
              "        [2.95394980e-08, 1.51481174e-07, 3.60858166e-07, ...,\n",
              "         3.60928141e-07, 1.52192243e-07, 2.83824679e-08],\n",
              "        [2.95282412e-08, 1.51473415e-07, 3.60873827e-07, ...,\n",
              "         3.60878374e-07, 1.52209395e-07, 2.84128063e-08]],\n",
              "\n",
              "       [[3.13212354e-08, 1.60806337e-07, 3.83106851e-07, ...,\n",
              "         3.83054953e-07, 1.61560237e-07, 3.02324459e-08],\n",
              "        [3.13118775e-08, 1.60801704e-07, 3.83119044e-07, ...,\n",
              "         3.83015902e-07, 1.61568977e-07, 3.02572580e-08],\n",
              "        [3.13037170e-08, 1.60798265e-07, 3.83129361e-07, ...,\n",
              "         3.82982876e-07, 1.61574775e-07, 3.02788337e-08],\n",
              "...\n",
              "         1.96293385e-07, 8.29505780e-08, 1.50487178e-08],\n",
              "        [1.62505316e-08, 8.22600583e-08, 1.96167846e-07, ...,\n",
              "         1.96239867e-07, 8.29732159e-08, 1.50812127e-08],\n",
              "        [1.62392162e-08, 8.22522637e-08, 1.96183407e-07, ...,\n",
              "         1.96189973e-07, 8.29903755e-08, 1.51115351e-08]],\n",
              "\n",
              "       [[1.05648432e-08, 5.27654045e-08, 1.26057728e-07, ...,\n",
              "         1.26003528e-07, 5.35219762e-08, 9.47580236e-09],\n",
              "        [1.05555431e-08, 5.27607860e-08, 1.26069907e-07, ...,\n",
              "         1.25964405e-07, 5.35307194e-08, 9.50063228e-09],\n",
              "        [1.05474376e-08, 5.27573754e-08, 1.26080195e-07, ...,\n",
              "         1.25931450e-07, 5.35365245e-08, 9.52222301e-09],\n",
              "        ...,\n",
              "        [1.05985629e-08, 5.27890300e-08, 1.26011244e-07, ...,\n",
              "         1.26151818e-07, 5.34696589e-08, 9.38519040e-09],\n",
              "        [1.05865086e-08, 5.27792565e-08, 1.26028226e-07, ...,\n",
              "         1.26098271e-07, 5.34923004e-08, 9.41770306e-09],\n",
              "        [1.05752127e-08, 5.27714903e-08, 1.26043773e-07, ...,\n",
              "         1.26048405e-07, 5.35094600e-08, 9.44804235e-09]]],\n",
              "      shape=(8, 128, 64), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_component_of_wind</span></div><div class='xr-var-dims'>(sigma, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.75e-13 -1.704e-13 ... 6.613e-15</div><input id='attrs-84ed0d01-a38c-4388-bbe6-65b239476740' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84ed0d01-a38c-4388-bbe6-65b239476740' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b28db57d-2b44-438c-804e-028469254687' class='xr-var-data-in' type='checkbox'><label for='data-b28db57d-2b44-438c-804e-028469254687' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.75033883e-13, -1.70356838e-13, -1.92637830e-13, ...,\n",
              "         -8.54348872e-14, -6.78000204e-14, -5.31479191e-14],\n",
              "        [-1.72298794e-13, -1.69057422e-13, -1.96051522e-13, ...,\n",
              "         -8.75018170e-14, -6.92273794e-14, -5.39339962e-14],\n",
              "        [-1.69223563e-13, -1.67678656e-13, -1.99490028e-13, ...,\n",
              "         -8.85313483e-14, -7.00451456e-14, -5.44606915e-14],\n",
              "        ...,\n",
              "        [-1.81057074e-13, -1.73858025e-13, -1.83787867e-13, ...,\n",
              "         -7.35507575e-14, -5.99595921e-14, -4.93218171e-14],\n",
              "        [-1.79428439e-13, -1.72769378e-13, -1.86447849e-13, ...,\n",
              "         -7.83846729e-14, -6.31418068e-14, -5.08304065e-14],\n",
              "        [-1.77415361e-13, -1.71594875e-13, -1.89406230e-13, ...,\n",
              "         -8.23736424e-14, -6.57658268e-14, -5.21100022e-14]],\n",
              "\n",
              "       [[ 1.64551830e-13,  1.32583980e-13,  1.07908291e-13, ...,\n",
              "         -7.29603688e-14, -8.31239171e-14, -6.48781566e-14],\n",
              "        [ 1.68761408e-13,  1.36383065e-13,  1.08958280e-13, ...,\n",
              "         -7.07533329e-14, -8.24531415e-14, -6.39778215e-14],\n",
              "        [ 1.72580605e-13,  1.39834813e-13,  1.09605901e-13, ...,\n",
              "         -6.74103582e-14, -8.09418721e-14, -6.27079972e-14],\n",
              "...\n",
              "         -8.86346626e-15, -1.68267362e-15,  9.02692117e-16],\n",
              "        [ 1.07011784e-13,  1.06349879e-13,  1.00825063e-13, ...,\n",
              "         -1.22967796e-14, -3.93009227e-15, -2.23421722e-16],\n",
              "        [ 1.11287966e-13,  1.11527066e-13,  1.05809255e-13, ...,\n",
              "         -1.55484824e-14, -6.09420427e-15, -1.34974499e-15]],\n",
              "\n",
              "       [[ 4.24285613e-14,  3.20754911e-14,  2.90346902e-14, ...,\n",
              "          1.52322037e-14,  1.10842223e-14,  8.72234719e-15],\n",
              "        [ 4.24306281e-14,  3.13596906e-14,  2.76918042e-14, ...,\n",
              "          1.63496654e-14,  1.29346920e-14,  1.07789838e-14],\n",
              "        [ 4.23750797e-14,  3.06880511e-14,  2.65343930e-14, ...,\n",
              "          1.72858079e-14,  1.46971075e-14,  1.27767517e-14],\n",
              "        ...,\n",
              "        [ 4.20144842e-14,  3.41054191e-14,  3.31918873e-14, ...,\n",
              "          1.03437775e-14,  4.91263143e-15,  2.25952688e-15],\n",
              "        [ 4.22269539e-14,  3.34883962e-14,  3.18870127e-14, ...,\n",
              "          1.22612712e-14,  7.07957888e-15,  4.45612260e-15],\n",
              "        [ 4.23628045e-14,  3.27983083e-14,  3.04673482e-14, ...,\n",
              "          1.38824363e-14,  9.13521900e-15,  6.61263841e-15]]],\n",
              "      shape=(8, 128, 64), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c9ff697-e209-42c1-aea2-21bcd524afa8' class='xr-section-summary-in' type='checkbox'  ><label for='section-6c9ff697-e209-42c1-aea2-21bcd524afa8' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3ca361ad-bbe5-4b01-bb23-fa7ec04e5a53' class='xr-index-data-in' type='checkbox'/><label for='index-3ca361ad-bbe5-4b01-bb23-fa7ec04e5a53' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0.0,   2.8125,    5.625,   8.4375,    11.25,  14.0625,   16.875,\n",
              "        19.6875,     22.5,  25.3125,\n",
              "       ...\n",
              "        331.875, 334.6875,    337.5, 340.3125,  343.125, 345.9375,   348.75,\n",
              "       351.5625,  354.375, 357.1875],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=128))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c7ea9583-1482-4974-a5b5-4b2eee4068b0' class='xr-index-data-in' type='checkbox'/><label for='index-c7ea9583-1482-4974-a5b5-4b2eee4068b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -87.86379883923263,   -85.0965269883173,  -82.31291294788628,\n",
              "        -79.52560657265944,  -76.73689968036831,  -73.94751515398967,\n",
              "        -71.15775201158732,  -68.36775610831317,  -65.57760701082782,\n",
              "       -62.787351798963066,   -59.9970201084913, -57.206631527643246,\n",
              "        -54.41619952608621,  -51.62573367493825,  -48.83524096625059,\n",
              "       -46.044726631101675,  -43.25419466535093,  -40.46364817811504,\n",
              "        -37.67308962904533, -34.882520993773454,  -32.09194388174401,\n",
              "       -29.301359621762735, -26.510769325210994, -23.720173933534753,\n",
              "        -20.92957425448951, -18.138970990239358, -15.348364759491494,\n",
              "       -12.557756115230678,  -9.767145559195567,  -6.976533553948635,\n",
              "       -4.1859205331891545, -1.3953069108194969,  1.3953069108194969,\n",
              "        4.1859205331891545,   6.976533553948635,   9.767145559195567,\n",
              "        12.557756115230678,  15.348364759491494,  18.138970990239358,\n",
              "         20.92957425448951,  23.720173933534753,  26.510769325210994,\n",
              "        29.301359621762735,   32.09194388174401,  34.882520993773454,\n",
              "         37.67308962904533,   40.46364817811504,   43.25419466535093,\n",
              "        46.044726631101675,   48.83524096625059,   51.62573367493825,\n",
              "         54.41619952608621,  57.206631527643246,    59.9970201084913,\n",
              "        62.787351798963066,   65.57760701082782,   68.36775610831317,\n",
              "         71.15775201158732,   73.94751515398967,   76.73689968036831,\n",
              "         79.52560657265944,   82.31291294788628,    85.0965269883173,\n",
              "         87.86379883923263],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sigma</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-759b4c03-2588-4f32-aee0-8ca3e531b382' class='xr-index-data-in' type='checkbox'/><label for='index-759b4c03-2588-4f32-aee0-8ca3e531b382' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0625, 0.1875, 0.3125, 0.4375, 0.5625, 0.6875, 0.8125, 0.9375], dtype=&#x27;float32&#x27;, name=&#x27;sigma&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5407cc20-3ce2-4c85-9a61-b16d27b92be0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5407cc20-3ce2-4c85-9a61-b16d27b92be0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
            ],
            "text/plain": [
              "<xarray.Dataset> Size: 1MB\n",
              "Dimensions:               (longitude: 128, latitude: 64, sigma: 8)\n",
              "Coordinates:\n",
              "  * longitude             (longitude) float64 1kB 0.0 2.812 ... 354.4 357.2\n",
              "  * latitude              (latitude) float64 512B -87.86 -85.1 ... 85.1 87.86\n",
              "  * sigma                 (sigma) float32 32B 0.0625 0.1875 ... 0.8125 0.9375\n",
              "Data variables:\n",
              "    cos_latitude          (longitude, latitude) float32 33kB 0.03728 ... 0.03728\n",
              "    sin_latitude          (longitude, latitude) float32 33kB -0.9993 ... 0.9993\n",
              "    vorticity             (sigma, longitude, latitude) float32 262kB -2.357e-...\n",
              "    divergence            (sigma, longitude, latitude) float32 262kB 1.369e-1...\n",
              "    log_surface_pressure  (longitude, latitude) float32 33kB 11.51 ... 11.51\n",
              "    temperature           (sigma, longitude, latitude) float32 262kB 217.6 .....\n",
              "    u_component_of_wind   (sigma, longitude, latitude) float32 262kB 2.952e-0...\n",
              "    v_component_of_wind   (sigma, longitude, latitude) float32 262kB -1.75e-1..."
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axarray = plt.subplots(2, 4, figsize=(18, 6))\n",
        "sigma_idx = -1\n",
        "for var, ax in zip(features_ds, axarray.ravel()):\n",
        "  if 'sigma' in features_ds[var].dims:\n",
        "    features_ds[var].isel(sigma=sigma_idx).plot(x='longitude', y='latitude', ax=ax)\n",
        "  else:\n",
        "    features_ds[var].plot(x='longitude', y='latitude', ax=ax)\n",
        "  ax.set_title(var)\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "id": "7dZqKapfBSq4",
        "colab": {
          "height": 440
        },
        "outputId": "e7f79b74-5e08-4f4b-f0d5-d0e39cd0c21e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1296x432 with 16 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 423,
              "width": 1283
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quick analysis like this can help easily identify lack of feature normalization or other artifacts."
      ],
      "metadata": {
        "id": "VNuLFgMgCLee"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
